Volume 10 Number 2 2026
Computational drug repurposing for amyotrophic lateral sclerosis, from transcriptomic prediction to pharmacological validation
Ghaith Kamil Jawad 1*, Abdulsamie Hassan Alta'ee 2 , Oday Jasim Alsalihi 3, Ruslan Rakhmanov 4, Anvar Takhirov 5
Integrative Biomedical Research 10 (2) 1-8 https://doi.org/10.25163/biomedical.10210958
Submitted: 08 September 2026 Revised: 25 October 2026 Accepted: 04 November 2026 Published: 06 November 2026
Amyotrophic lateral sclerosis (ALS) remains one of the most unforgiving diseases in neurology. Motor neurons in the cortex, brainstem, and spinal cord degenerate relentlessly, and most patients die of respiratory failure within two to five years of onset. Riluzole, edaravone, and tofersen are the only approved disease-modifying agents, yet their benefits are modest or confined to a small genetic subgroup. This gap has pushed researchers toward drug repurposing, and increasingly toward computational methods for doing it. In this structured narrative review of 34 sources, we examine how transcriptome-wide machine-learning ensembles, weighted gene co-expression networks, connectivity mapping, single-cell Mendelian randomization, and knowledge-graph approaches have been used to nominate repurposable drugs for ALS. We also place these predictions alongside recent fluid-biomarker, clinical, and neuroimaging evidence. Four pipelines stand out, each drawing on a different tissue. They nominated deferoxamine and disulfiram (metal and redox stress), memantine and other SMN1-network modulators (RNA metabolism), clonidine and fingolimod (immune-metabolic signalling), and glibenclamide, tamoxifen, and quercetin (cytotoxic CD4? T-cell inflammation). The picture is less reassuring once specificity is tested. Motor-cortex and blood signatures barely overlap. A blood signature that performed well against healthy controls fell to chance level (AUC = 0.525) against disease mimics. Perturbation data come largely from cancer cell lines, and almost no candidate has been checked for potency, blood–brain barrier penetration, or dose–response. We argue that mimic-inclusive modelling, validation in human iPSC-derived and organoid systems, and quantitative pharmacokinetic–pharmacodynamic work are needed before these computational leads can reasonably be treated as therapeutic candidates rather than well-formed hypotheses.
Keywords: amyotrophic lateral sclerosis; drug repurposing; systems pharmacology; machine learning; connectivity map; Mendelian randomization; WGCNA; translational neurology
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